a0422e404e
The interpreter doesn't call it if a nullptr is presented in the registration. This can slightly reduce code size. PiperOrigin-RevId: 304284594 Change-Id: I7fa092aa9cf5142ee30982d07655971fd1626630
129 lines
4.3 KiB
C++
129 lines
4.3 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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namespace tflite {
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namespace ops {
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namespace micro {
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namespace split {
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template <typename T>
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TfLiteStatus SplitImpl(TfLiteContext* context, TfLiteNode* node,
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const TfLiteTensor* input, int axis_value) {
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const int output_count = NumOutputs(node);
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const TfLiteIntArray* input_dims = input->dims;
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const TfLiteTensor* output0 = GetOutput(context, node, 0);
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const TfLiteIntArray* output_dims = output0->dims;
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const int split_dimensions = input_dims->size;
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int axis = axis_value < 0 ? axis_value + split_dimensions : axis_value;
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TFLITE_DCHECK_LT(axis, split_dimensions);
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TFLITE_DCHECK_EQ(output_dims->size, split_dimensions);
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int64_t split_size = output_dims->data[axis] * output_count;
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TFLITE_DCHECK_EQ(split_size, input_dims->data[axis]);
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int64_t outer_size = 1;
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for (int i = 0; i < axis; ++i) {
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outer_size *= input_dims->data[i];
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}
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int64_t base_inner_size = 1;
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for (int i = axis + 1; i < split_dimensions; ++i) {
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base_inner_size *= input_dims->data[i];
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}
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const T* input_ptr = GetTensorData<T>(input);
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for (int k = 0; k < outer_size; ++k) {
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for (int i = 0; i < output_count; ++i) {
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TfLiteTensor* t = GetOutput(context, node, i);
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T* output_data = GetTensorData<T>(t);
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const int copy_size = output_dims->data[axis] * base_inner_size;
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T* output_ptr = output_data + k * copy_size;
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for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j];
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input_ptr += copy_size;
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}
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}
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* axis = GetInput(context, node, 0);
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const TfLiteTensor* input = GetInput(context, node, 1);
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// Dynamic output tensors are needed if axis tensor is not constant.
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// But Micro doesn't support dynamic memory allocation, so we only support
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// constant axis tensor for now.
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TF_LITE_ENSURE_MSG(context, IsConstantTensor(axis),
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"Non constant axis tensor not supported");
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int axis_value = GetTensorData<int32_t>(axis)[0];
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if (axis_value < 0) {
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axis_value += NumDimensions(input);
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}
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TF_LITE_ENSURE(context, axis_value >= 0);
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TF_LITE_ENSURE(context, axis_value < NumDimensions(input));
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switch (input->type) {
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case kTfLiteFloat32: {
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return SplitImpl<float>(context, node, input, axis_value);
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}
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case kTfLiteUInt8: {
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return SplitImpl<uint8_t>(context, node, input, axis_value);
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}
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case kTfLiteInt8: {
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return SplitImpl<int8_t>(context, node, input, axis_value);
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}
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case kTfLiteInt16: {
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return SplitImpl<int16_t>(context, node, input, axis_value);
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}
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case kTfLiteInt32: {
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return SplitImpl<int32_t>(context, node, input, axis_value);
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}
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default:
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TF_LITE_KERNEL_LOG(context, "Type %s currently not supported.",
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TfLiteTypeGetName(input->type));
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return kTfLiteError;
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}
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#undef TF_LITE_SPLIT
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return kTfLiteOk;
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}
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} // namespace split
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TfLiteRegistration* Register_SPLIT() {
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static TfLiteRegistration r = {/*init=*/nullptr,
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/*free=*/nullptr,
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/*prepare=*/nullptr,
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/*invoke=*/split::Eval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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return &r;
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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